## [1] FALSE
column means:
## V1 V2 V3 V4 V5 V6
## 0.4687 0.3410 0.4216 0.2729 0.5865 0.4951
column ggcorr:
column means:
## V1 V2 V3 V4 V5 V6
## 0.4670 0.5113 0.5735 0.6589 0.4148 0.4459
column ggcorr:
column means:
## V1 V2 V3 V4 V5 V6
## -0.001714 0.170327 0.151924 0.386049 -0.171691 -0.049165
column ggcorr:
## Call:
## lda(dat, grouping = clas)
##
## Prior probabilities of groups:
## cl a cl b
## 0.6667 0.3333
##
## Group means:
## V1 V2 V3 V4 V5 V6
## cl a 0.4687 0.3410 0.4216 0.2729 0.5865 0.4951
## cl b 0.4670 0.5113 0.5735 0.6589 0.4148 0.4459
##
## Coefficients of linear discriminants:
## LD1
## V1 40.165
## V2 -22.355
## V3 21.999
## V4 9.730
## V5 -67.496
## V6 1.187
## Standard deviations (1, .., p=6):
## [1] 0.30259 0.26919 0.23593 0.14323 0.07119 0.04732
##
## Rotation (n x k) = (6 x 6):
## PC1 PC2 PC3 PC4 PC5 PC6
## V1 -0.0647 0.66301 -0.194811 0.20382 -0.3713 0.58212
## V2 0.4831 -0.05549 0.286220 -0.35724 -0.7329 -0.12976
## V3 0.4539 -0.14771 -0.707198 -0.41531 0.1923 0.25010
## V4 0.6968 0.16442 0.008681 0.63921 0.1772 -0.21774
## V5 -0.2068 0.38166 -0.489201 -0.05886 -0.1987 -0.72753
## V6 -0.1678 -0.60235 -0.374973 0.49628 -0.4659 0.07096
Original variable cluster seperation:
Original vs MMP cluster seperation:
MMP cluster seperation: